Data Science



Healthcare prediction

A medical emergency could arise at any moment and it must be nursed immediately. We must know the symptoms to determine the condition of the subject. This prediction analysis will avoid any inevitable results from happening. It offers healthcare providers and patients the chance to get ahead of common financial, clinical, and administrative concerns.

Client Profile

It is a Proof of Concept that was worked in a certain process to analyze the feasibility and the practical potential for real world application of the project proposed.


The primary objective of the project is to identify the hospital that provides the critical care needed for the patients with high benefits and standards in their neighbourhood.

Key Challenges

The key challenge that was faced during the process was the data analysis and data collection of various specialities. In addition to it, the ratings and reviews of the hospitals were taken into consideration from various blogs.

Our Approach

The intense challenge of pre-processing the data was dealt with rescaling and standardizing all the provided data. A classification module was created based upon the ratings, habitat and emergency services.

Our Solution

The collection of data was stored inside a repository and extracted by Rest API to provide better results with accuracy. Classifier was developed based on the provided criteria and the criticality was valued by the given class under the flag set of disease field. Various choices were provided in favor of locality. Additionally, the hospital data was also provided by the application from which the user can select their comfort zone. And subsequently, it alarms the user whether a criticality of the disease is pertained.


An optimum health care application is developed for aiding the users in an emergency along with the state of the sickness. It is also developed in an approach that reduces space, time complexities and results with high accuracy in determining the locality of the emergency provider with brief details.

Technology Used

  • Back-end: Python, Support Vector Machine.
  • API: Rest API
  • Front-End: Django


The emergency situations are handled well without creating a panic through a provider. The residing state, condition of the patient, type of the hospital makes the users aware in an extreme crisis. It is a great boon to the patients who needs serious assistance.

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